The search for subsurface hydrocarbon deposits typically involves a sequence of seismic data acquisition, analysis, and interpretation. The data acquisition phase involves use of an energy source to generate signals which propagate into the earth and reflect from various subsurface geologic structures. The reflected signals, referred to as traces, are recorded by a multitude of receivers on or near the surface of the earth, or in an overlying body of water. These signals are relied upon during the analysis phase to develop an image of the subsurface geologic structures.
The analysis phase involves procedures which vary depending on the nature of the geological structure being investigated, and on the characteristics of the dataset itself. However, because the seismic traces will generally include both a signal component and a noise component, one routine aspect of this phase involves procedures directed at eliminating to the maximum extent possible the noise component in the traces. As is well understood to those skilled in the art, the quality of the output of the data processing phase is a function of the success of the noise elimination procedures.
The final phase is the interpretation of the analytic results. Specifically, the interpreter's task is to assess the extent to which subsurface hydrocarbon deposits are present, thereby aiding such decisions as whether additional exploratory drilling is warranted or what an optimum hydrocarbon recovery scenario may be. Again, as is clearly understood in the art, the quality and accuracy of the results of the noise elimination procedures have a significant impact on the accuracy and usefulness of the results of the interpretation phase. It is clear, therefore, that noise elimination is important in the seismic data processing industry.
Two types of noise are commonly present in seismic data: 1) random ambient noise, and 2) coherent linear noise. Coherent linear noise will often be a function of the location from which the data derives. For example, in offshore seismic data acquisition, the noise component of the received seismic traces will, among other sources, often include unwanted energy deriving from signals which are trapped in the water layer and reflect between the seafloor and the water surface. These signals are often referred to as water-layer-related multiples. Multiple attenuation is an important step in marine seismic data processing.
A variety of techniques have been implemented to attenuate water-layer multiples. One well known method of suppressing multiples focuses on the dip difference between the primary signal and the multiples in the common midpoint-stacked dataset. Dip differences in the time-space domain can be separated in the frequency-wavenumber domain based on frequency content. Once the recorded data are transformed to the frequency-wavenumber domain, a dip filter is applied to the data, and the data are inverse transformed back to the time-space domain. However, several limitations exist on this method. First, the Fourier transform can produce unwanted aliasing noise. Second, the dip filter must be appropriately chosen--an overly narrow filter bandwidth will not sufficiently filter out the multiples, whereas an overly wide filter will suppress desired signal frequencies. Finally, smearing of the desired signal frequencies can result from the transform/inverse transform procedures.
A second method of multiple suppression is referred to as predictive deconvolution. Predictive deconvolution relies on the time series periodicity of the multiples for discrimination of primary signal content from the multiples. Generally, the method involves use of an autocorrelation of the trace data to determine the periodicity of the multiples. Use of that periodicity in the deconvolution operator then allows generation of a signal containing only the desired primary signal. The principle limitation of this method is that multiple periodicity is only maintained for vertical incidence, zero-offset data, and therefore for other types of data, such as common-shot or common-midpoint data, the method is generally unsatisfactory.
An advanced form of predictive deconvolution is referred to as slant-stack multiple suppression. This method was developed to address the vertical incidence, zero-offset limitation of the basic predictive deconvolution procedure. The technique involves the same analytic approach as the basic procedure, except that the deconvolution is performed along radial traces which have constant angles of propagation. Those propagation angles lead to constant time separations between multiples along the radial traces, thereby allowing the deconvolution operator to focus on the periodicity along those traces. Although this procedure improves on the basic technique, its' accuracy is constrained by the extent to which the deconvolution operator length and prediction lag can be determined from the autocorrelation.
A fourth method of multiple suppression focuses on velocity discrimination in either the frequency-wavenumber domain or the time-space domain. These techniques rely on sufficient velocity differences existing between the primary and the multiples. For example, the frequency-wavenumber domain approach involves normal moveout correcting the data using a velocity between the primary and the multiple velocity. Thereafter, the sequence of transforming, filtering, and inverse transforming discussed above in conjunction with dip difference multiple suppression is carried out. Although an improvement upon the dip difference technique, this approach suffers that technique's limitations, specifically with respect to aliasing, filter selection, and data loss due to smearing.
Velocity discrimination in the time-space domain requires generation of a model trace for the multiples, which is then subtracted from individual traces of the normal moveout corrected gather. The result should only contain primary energy. The principle limitation is in generating an accurate model trace, i.e. one which does not contain some primary energy. Typically, the low frequency end of the primary spectrum will have velocities not significantly different than the multiples, and therefore the procedure can lead to loss of the low-frequency energy content of the primary.
In addition to the above limitations of prior art multiple suppression techniques, additional limitations exist which have not been adequately addressed by industry. First, all of the above techniques assume a relatively smooth seafloor. Because propagation and attenuation of multiples varies as seafloor irregularity becomes moderate or severe, a technique which takes into account that irregularity is needed by industry. Second, water bottom multiples also have characteristics which are dependent on water depth, such as phase velocity, due to their dispersive nature. Thus, techniques which accurately take into account water depth-dependent characteristics is required. Third, due to the inadequacies of presently available multiple attenuation methods, some analysts discard small offset traces, sometimes referred to as the near traces, because present multiple attenuation methods are particularly inadequate for those traces. As will be understood to those skilled in the art, however, those traces contain valuable seismic information and are therefore preferably retained for detailed seismic processing. A method which does not breakdown for small offset traces is desired by industry.
Finally, all of the above techniques are only applicable to two-dimensional analysis. In regions characterized by a moderate to highly irregular seafloor, three-dimensional analysis is required. Undulating seafloor surfaces create out-of-plane multiples that are three-dimensional and which cannot be resolved using existing technology. The more rigorous wave-equation-based methods which have been proposed to address those limitations are expensive, particularly in 3-D, and not yet proven to be effective.
Therefore, there is a need for an improved multiple suppression technique which takes into account irregular seafloor surfaces, which can be applied in three-dimensional analysis, and which can be cost effectively implemented. The present invention satisfies this need.